1TOPIC 12 IMAGE SEGMENTATION MORPHOLOGY 2SEGMENTATIONImage segmentation is approached from three different perspectives .Region detection each pixel is assigned to a particular object or region. .Boundary detection one attempts to detect the boundaries that exist between regions. .Edge detection one seeks to identify edge pixels and then link them together to form a connected boundary. 3THRESHOLDING 4THE LAPLACIAN OPERATOR 5THE LAPLACIAN OPERATOR IN PRACTICE 6PROPERTIES OF THE LAPLACIAN OPERATOR

If the area around the pixel has a constant grey level

then application of the kernel results in a grey level

value of 0

The average grey level is 0

Negative values are possible. In practice this can be

avoided by defining zero to be the grey value 128

The kernel is not direction sensitive in the sense of

preferentially selecting either vertical or horizontal

edges.

7(No Transcript) 8ALTERNATIVE FORM OF THE LAPLACIAN 9ROBERTS OPERATOR 10SOBEL OPERATOR 11THE SOBEL OPERATORThe Sobel operator is one of those most commonly used and has received special attention because it can be performed in real time (lt 1/30 sec. Per image) which makes it attractive for military tracking applications. 12The Canny Edge Detector

The Canny edge detector is an example of an optimal edge detector in that it claims to optimise the edge detecting process. Its operation is however quite complex.

It works as a multi stage process

The image is smoothed with a Gaussian convolution.

A simple 2D operator (similar to the Roberts) is applied to highlight regions with a high gradient.

Edges give rise to ridges in the gradient image the algorithm then tracks along these ridges marking a trail 1 pixel wide on the top of the ridge.

Two parameters control the process (a) which is the lowest point on a ridge that tracking begins and (b) blta is the point at which tracking ceases when the height of the ridge falls below this value.

13The Canny edge detector in actiona 0.0938b 0.0375 14(No Transcript) 15Edge detection in MATLABfunction UsingEdge To edge detect a butterfly A imread('moth9.gif') BW,thresh edge(A,'sobel',0.04) imshow(BW) threshThresh0.04Thresh0.06 16A MORPHOLOGICAL TRANSFORMATIONA morphological transformation is one where a structuring element (usually 3x3 but could be larger) is passed over the binary image and a logical operation performed (e.g. AND, OR, NOT, XOR in simple cases) on a pixel by pixel basis. The structuring element is also binary in form 17BASIC EROSIONThis is the process of eliminating all boundary points from an element. In other words if a pixel with a value 1 has an immediate neighbour with a value 0 the pixel value is set to 0. Note the pixel values are not continuously updated during the transformation otherwise the result would be a blank image ! 18EROSION APPLICATION 19FORMAL DEFINITION OF EROSION 20DILATIONDilation is the inverse of erosion where any pixel adjacent to a pixel of value 1 is changed to 1 21Thinning Erosion can be programmed as a two step process so that objects are not broken up. In the first step candidate pixels are marked for deletion but not deleted. In the second pass pixels are eliminated only if they would not destroy the connectivity of the object. Thinning reduces objects to a single pixel wide line highlighting line topology 22THINNING 23Opening The process of erosion followed by dilation is known as opening. It has the effect of eliminating small objects, smoothing the boundaries of large objects without significantly changing their area. In other words a good combination for cleaning up a segmented image. 24Closing The process of dilation followed erosion is known as closing. It has the effect of filling small and thin holes and smoothing boundaries without changing the area of larger objects. 25Shrinking If erosion is implemented in such a way that single pixel objects are left intact the process is known as shrinking. It is a useful way of counting the size distribution of objects in the scene by successive shrinking followed by counting the change in the number of single pixel objets in the image. 26Skeletonisation This process is very similar to thinning and can also be achieved by a two pass process. The essential difference is that skeletonisation retains the full dimension of the object. 27ANALYSIS PIPELINE 28UNDERSTANDING A PRINTED CIRCUIT BOARD 29Using convolution for filtering 30Convolution and CorrelationConvolutionFinds its principal application in high and low pass filteringCorrelationFinds its principal application in template matching 31Example of using filter2function UsingMask Edge detection using a mask A imread('moth9.gif') smooth the image mask 1 1 11 1 11 1 1 multiplier 1/(2559) mask multiplier.mask BW filter2(mask,A) imshow(BW)The mask 32An improved filtering mask to remove noise

33DILATION

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